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Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work.
AlphaFold-enabled structure prediction, generative small-molecule design (Insilico, Recursion/Exscientia, Isomorphic), de novo antibody design (Absci, Generate, Nabla), and multi-omic target identification (DualityBio's DB-1329 CDCP1 ADC was AI-nominated). First AI-designed oncology molecules are in phase 2; none approved yet.
Deep learning over sequence, structure, and omics; active learning with wet-lab loops.
Query for this technology: (TITLE:"AI drug discovery" OR ABSTRACT:"AI drug discovery" OR TITLE:"generative model" OR ABSTRACT:"generative model" OR TITLE:"de novo design" OR ABSTRACT:"de novo design") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about AI-driven drug & target discovery, not a curated reading list.
Shares Noetik, Pathos AI, AI compute and model platforms for oncology, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tag frontier.
Shares Molecular glue discovery platforms, ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs, Drug discovery roadmap: screening in mice → maps of dependency → designing in silico and the tag frontier.
Shares Pathos AI, ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs and the tag frontier.
Shares ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs, Too many combinations to test and the tag frontier.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, The undruggable drivers and the tag frontier.
Shares ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs and the tag frontier.
Shares ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs and the tag frontier.
Shares ADC roadmap: from Mylotarg to bispecific and dual-payload ADCs and the tag frontier.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
DeepMind's model of proteins with ligands, nucleic acids and modifications; code is released for non-commercial use and weights by request.
A Python library that democratises deep learning for drug discovery, materials and biology.
An open biomolecular structure and affinity prediction model from MIT and Recursion, released under MIT with weights.
The open cheminformatics toolkit that nearly all open drug discovery code depends on for molecules, fingerprints and descriptors.
MIT's message-passing neural networks for molecular property prediction, used in antibiotic and oncology screening papers.
Chai Discovery's multi-modal structure prediction model; code and weights are released, with commercial use permitted under its terms.
MIT's diffusion model for molecular docking with released weights.
Harvard's collection of machine-learning-ready datasets and benchmarks across the drug discovery pipeline.
Open-source software, hardware and data projects catalogued by a third party, the Open Medical Registry, that bear on this technology. Listing is not endorsement; check each project's own licence and validation before clinical use.
SynProtX is a deep learning model leveraging large-scale proteomics, molecular graphs, and fingerprints to enhance the prediction of synergistic effects in...
From the Open Medical Registry (openmedical.sh), an MIT-licensed catalogue of open-source medicine. Blurbs are one line from each registry record; every project keeps its own licence.